docs: def analyze(self, count):
          """ Analyze count data from :meth:`PDFHistogram.count`.
  
          Turns an array of counts (see :meth:`PDFHistogram.count`) into a
          histogram of probabilities, and  estimates the mean, standard
          deviation, and other statistical characteristics of the corresponding
          probability distribution.
  
          Args:
              count (array): Array of length ``nbin+2`` containing histogram
                  data where ``count[0]`` is the count for values that are
                  below the range of the histogram, ``count[-1]`` is the count
                  for values above the range, and ``count[i]`` is the count
                  for the ``i``-th bin where ``i=1...nbin``.
  
          Returns a named tuple containing the following information (in order):
  
              *bins*: Array of bin edges for histogram (length ``nbin+1``)
  
              *prob*: Array of probabilities for each bin.
  
              *stats*: Statistical data about histogram. See :class:`PDFStatistics`.
  
              *norm*: Convert counts into probabilities by dividing by ``norm``.
          """
          if numpy.ndim(count) != 1:
              raise ValueError('count must have dimension 1')
          if len(count) == len(self.midpoints) + 2:
              norm = numpy.sum(count)
              data = numpy.asarray(count[1:-1]) / norm
          elif len(count) != len(self.midpoints):
              raise ValueError(
                  'wrong data length: %s != %s'
                      % (len(count), len(self.midpoints))
                  )
          else:
              data = count
              norm = 1.
          mid = self.midpoints
          stats = PDFStatistics(histogram=(self.bins, count))
          return PDFHistogram.Histogram(self.bins, data, stats, norm)